Higher-Order Weighted Perturbation-Based Multilevel Information Fusion Model for Predicting CircRNA–Disease Associations

CircRNAs are closely associated with the initiation and progression of various diseases, and investigating their potential associations is crucial for understanding disease mechanisms. Although existing computational methods have made notable progress, they often overlook the latent information contained in higher-order associations between circRNAs and diseases. Moreover, these methods often rely on a single linear or nonlinear feature learning approach, which fails to comprehensively capture multilevel features. To address this, a high-order weighted perturbation-based multilevel information fusion model (HWP-MIFM) is proposed. The model dynamically adjusts the weights of different orders using a higher-order weighted perturbation method to extract higher-order association information. A dual-stage matrix factorization module is applied to construct a multilayer structure and extract linear features. Additionally, a dual-path feature learning module is utilized to dig complex nonlinear relationships within similarity networks, ensuring the comprehensive capture of multilevel features. Experimental results demonstrate that, in 5-fold cross-validation on four data sets, HWP-MIFM outperforms seven state-of-the-art prediction methods in terms of overall performance. Ablation studies and case analyses further confirm the accuracy and practical value of the model.